Marlena Duda

Advisors

Areas of Interest

Currently, the process for detecting most neuropsychiatric conditions is largely subjective and relies solely on a short behavioral assessment. Studies investigating the biological basis of these disorders, including whole genome sequencing, gene expression analyses and various medical imaging studies, have identified significant differences between cases and controls. Despite this depth and breadth of investigation, most conditions still have no immediately translational biomarker candidates. In my proposed thesis work, I plan to utilize machine learning techniques on integrated data sets that include high throughput molecular data and neuroimaging data, as well as classic behavioral measures to 1) develop algorithms to improve the screening and diagnosis of these disorders and 2) gain insights into the etiology underlying these conditions.